# A Comparison of Parallel Graph Processing Implementations

## TL;DR

This paper introduces a systematic approach and software tool for evaluating and comparing the performance and scalability of various parallel graph processing systems using multiple metrics and datasets.

## Contribution

It provides a new methodology and software for rigorous, repeatable comparison of parallel graph libraries, including analysis of execution phases and energy consumption.

## Key findings

- Identified performance differences among five graph processing packages.
- Analyzed energy usage and execution phases for key algorithms.
- Compared results with existing benchmarks like Graphalytics.

## Abstract

The rapidly growing number of large network analysis problems has led to the emergence of many parallel and distributed graph processing systems---one survey in 2014 identified over 80. Since then, the landscape has evolved; some packages have become inactive while more are being developed. Determining the best approach for a given problem is infeasible for most developers. To enable easy, rigorous, and repeatable comparison of the capabilities of such systems, we present an approach and associated software for analyzing the performance and scalability of parallel, open-source graph libraries. We demonstrate our approach on five graph processing packages: GraphMat, the Graph500, the Graph Algorithm Platform Benchmark Suite, GraphBIG, and PowerGraph using synthetic and real-world datasets. We examine previously overlooked aspects of parallel graph processing performance such as phases of execution and energy usage for three algorithms: breadth first search, single source shortest paths, and PageRank and compare our results to Graphalytics.